In the field of high performance computing, the architectures evolve continuously. In order to increase the number of computing nodes or the network speed, an important investment must be considered, from both temporal and financial point of view. Performance prediction methods aim at assisting in finding the best trade-off for such an investment. At the same time, P2P HPC systems have known an increase in development. These heterogeneous architectures would allow solving scientific problems at a low cost, with respect to dedicated systems.The manuscript presents a new method for performance prediction. This method applies to real applications for distributed computing, considered in a real execution environment. This method uses information about the different compiler optimization levels. The prediction results are obtained with reduced slowdown and are scalable. This thesis took shape in the development of the dPerf tool. dPerf predicts the performances of C, C++, and Fortran application, which use MPI or P2P-SAP to communicate. The applications modeled by dPerf are meant for execution on P2P heterogeneous architectures, with a decentralized communication topology. The accuracy of dPerf has been studied on three applications: (i) the Laplace transform, for sequential codes, (ii) the NAS Integer Sort benchmark for distributed MPI programs, (iii) and the obstacle problem, for the decentralized P2P computing and the scaling of the number of computing nodes.
